# Dual-Selective Network for Domain-Incremental Change Detection

arXiv: https://arxiv.org/abs/2607.02299 · HTML: https://mlsanity.com/paper/2607.02299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [RS-Mamba for Large Remote Sensing Image Dense Prediction](https://mlsanity.com/paper/2404.02668/) (2024)
- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2404.09178/) (2024)
- [ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via α-β-Divergence](https://mlsanity.com/paper/2505.04560/) (2025)
- [Applications of Knowledge Distillation in Remote Sensing: A Survey](https://mlsanity.com/paper/2409.12111/) (2024)

## Abstract (arXiv)

Domain-incremental change detection (DICD) continuously adapts models to new geographic domains while preserving prior knowledge. However, a structural mismatch exists: the label space remains fixed while domain characteristics vary drastically. Consequently, incremental models struggle to maintain stable spatial change representations across domains. Existing strategies, such as replay-based or regularization-based methods, often fail to scale to long domain sequences, leading to knowledge degradation or increased computational cost. We propose Dual-Selective Incremental Network (DSINet), a unified framework built on visual state space models. DSINet leverages Mamba's input-dependent selective mechanism through a selective spatial state unit (S3U). This unit preserves stable spatial change structures while filtering domain-specific variations during feature propagation. As a result, spatial representations remain stable across domains, preventing the accumulation of feature confusion over incremental steps. Additionally, we employ a concentration-balanced distillation (CBD) strategy to stabilize knowledge transfer across domains. It balances hardness and confidence concentration effects during incremental updates. This ensures reliable probability mass allocation and prevents over-smoothing or mode collapse during distillation. Together, these mechanisms maintain stable learning dynamics throughout incremental stages. Experimental results demonstrate that DSINet mitigates knowledge degradation across long domain sequences while maintaining the linear computational efficiency of state space models.